The overall goal of the Arthritis Genomics and Bioinformatics Core will be to support investigators in the[unreadable] Program utilizing microarray techniques. The core will provide uniform sample preparation, microarray[unreadable] design, data storage and bioinformatics, thus maximizing the reliability and compatibility of information[unreadable] obtained across the Program. The Core will perform standardized RNA preparation from joint tissues of[unreadable] experimental animals, and the amplification steps to generate labeled probe from small amounts of in vivo[unreadable] or in vitro derived RNA. This should help to generate highly comparable datasets throughout the Program.[unreadable] In addition, the Core will design a study-specific "Arthritis Chip", with spotted oligonucleotides that[unreadable] represent genes that vary during arthritogenesis. These custom chips will provide flexibility, allow a greater[unreadable] throughput than would be economically feasible with whole-genome chips, and again enhance data[unreadable] compatibility between the groups. The Core's bioinformatics personnel, which have acquired significant[unreadable] experience in microarray analysis, will guide, train, and help investigators through data analysis in an open[unreadable] and collaborative manner. The Core will provide access to basic analysis packages, and the ability to write[unreadable] custom software in a data-driven manner, in response to particular experimental situations. In addition,[unreadable] data analysis will benefit from a baseline of microarray data on gene expression during arthritis. A central[unreadable] server for secure data storage will house copies of the data, and Web-based software for data browsing will[unreadable] be applied as a Program-specific intranet and for public posting, as appropriate. In addition, the Core will[unreadable] perform data mining on the assembled data, explorations made possible by the coordinated processing[unreadable] and storage of the data, and by our pre-existing data on the evolution of gene expression in arthritic joints.[unreadable] Cross-experiment analyses will search for gene-gene correlations and clusters throughout a variety of[unreadable] conditions, and will generate functional gene hierarchies. These meta-analyses will enrich each of the[unreadable] individual analyses: defining, for example, the subset of genes that are still induced in spite of a genetic[unreadable] blockade of arthritis will help to pinpoint the cellular or functional level at which the gene is implicated. In[unreadable] addition, the meta-analyses made possible by the combined and coordinated datasets will also provide a[unreadable] unique insight into the "arthritis genome".